In [1]:
"""Template for showing the results of the last experiment in MLFlow."""

import logging
import numpy as np
import helpsk as hlp
import pandas as pd
import plotly_express as px
from helpsk.utility import read_pickle, Timer
from helpsk.sklearn_eval import MLExperimentResults

from source.service.model_registry import ModelRegistry

%cd /code

from source.config import config  # noqa: E402
logging.config.fileConfig(
    "source/config/logging_to_file.conf",
    defaults={'logfilename': 'output/log.log'},
    disable_existing_loggers=False,
)
/usr/local/lib/python3.11/site-packages/IPython/core/magics/osm.py:417: UserWarning: using dhist requires you to install the `pickleshare` library.
  self.shell.db['dhist'] = compress_dhist(dhist)[-100:]
/code

Get Latest Experiment Run from MLFlow¶

In [2]:
registry = ModelRegistry(tracking_uri=config.experiment_server_url())
experiment = registry.get_experiment_by_name(exp_name=config.experiment_name())
logging.info(f"Experiment id: {experiment.last_run.exp_id}")
logging.info(f"Experiment name: {experiment.last_run.exp_name}")
logging.info(f"Run id: {experiment.last_run.run_id}")
logging.info(f"Metric(s): {experiment.last_run.metrics}")
2023-11-24 20:59:39 - INFO     | Experiment id: 1
2023-11-24 20:59:39 - INFO     | Experiment name: credit
2023-11-24 20:59:39 - INFO     | Run id: 7c18722134d54a99991126ac0a1c2971
2023-11-24 20:59:39 - INFO     | Metric(s): {'roc_auc': 0.753377535324465}

Last Run vs Production¶

What is the metric/performance from the model associated with the last run?

In [3]:
logging.info(f"last run metrics: {experiment.last_run.metrics}")
2023-11-24 20:59:39 - INFO     | last run metrics: {'roc_auc': 0.753377535324465}

What is the metric/performance of the model in production?

In [4]:
production_run = registry.get_production_run(model_name=config.model_name())
logging.info(f"production run metrics: {production_run.metrics}")
2023-11-24 20:59:39 - INFO     | production run metrics: {'roc_auc': 0.753377535324465}

Last Run¶

In [5]:
# underlying mlflow object
experiment.last_run.mlflow_entity
Out[5]:
<Run: data=<RunData: metrics={'roc_auc': 0.753377535324465}, params={'prep__numeric__imputer__transformer': 'SimpleImputer()',
 'prep__numeric__pca__transformer': 'None',
 'prep__numeric__scaler__transformer': 'None',
 'prep__savings_status__savings_encoder__transformer': "OneHotEncoder(handle_unknown='ignore')"}, tags={'mlflow.log-model.history': '[{"run_id": "7c18722134d54a99991126ac0a1c2971", '
                             '"artifact_path": "model", "utc_time_created": '
                             '"2023-11-24 20:59:35.369562", "flavors": '
                             '{"python_function": {"model_path": "model.pkl", '
                             '"predict_fn": "predict", "loader_module": '
                             '"mlflow.sklearn", "python_version": "3.11.6", '
                             '"env": {"conda": "conda.yaml", "virtualenv": '
                             '"python_env.yaml"}}, "sklearn": '
                             '{"pickled_model": "model.pkl", '
                             '"sklearn_version": "1.3.2", '
                             '"serialization_format": "cloudpickle", "code": '
                             'null}}, "model_uuid": '
                             '"8c19cae6bc014da3b8e7ada65bda5e94", '
                             '"mlflow_version": "2.8.0", "model_size_bytes": '
                             '15618339}]',
 'mlflow.note.content': '2023_11_24_20_59_08',
 'mlflow.runName': '2023_11_24_20_59_08',
 'mlflow.source.git.commit': '81a963fcbc4794b8b7bc6c330fc6b034760eb65d',
 'mlflow.source.name': 'source/entrypoints/cli.py',
 'mlflow.source.type': 'LOCAL',
 'mlflow.user': 'root',
 'type': 'BayesSearchCV'}>, info=<RunInfo: artifact_uri='/code/mlflow-artifact-root/1/7c18722134d54a99991126ac0a1c2971/artifacts', end_time=1700859577033, experiment_id='1', lifecycle_stage='active', run_id='7c18722134d54a99991126ac0a1c2971', run_name='2023_11_24_20_59_08', run_uuid='7c18722134d54a99991126ac0a1c2971', start_time=1700859548079, status='FINISHED', user_id='root'>, inputs=<RunInputs: dataset_inputs=[]>>

Load Training & Test Data Info¶

In [6]:
with Timer("Loading training/test datasets"):
    X_train = experiment.last_run.download_artifact(artifact_name='x_train.pkl', read_from=read_pickle)  # noqa
    X_test = experiment.last_run.download_artifact(artifact_name='x_test.pkl', read_from=read_pickle)  # noqa
    y_train = experiment.last_run.download_artifact(artifact_name='y_train.pkl', read_from=read_pickle)  # noqa
    y_test = experiment.last_run.download_artifact(artifact_name='y_test.pkl', read_from=read_pickle)  # noqa
Timer Started: Loading training/test datasets
Timer Finished (0.01 seconds)
In [7]:
logging.info(f"training X shape: {X_train.shape}")
logging.info(f"training y length: {len(y_train)}")

logging.info(f"test X shape: {X_test.shape}")
logging.info(f"test y length: {len(y_test)}")
2023-11-24 20:59:39 - INFO     | training X shape: (800, 20)
2023-11-24 20:59:39 - INFO     | training y length: 800
2023-11-24 20:59:39 - INFO     | test X shape: (200, 20)
2023-11-24 20:59:39 - INFO     | test y length: 200
In [8]:
np.unique(y_train, return_counts=True)
Out[8]:
(array([0, 1]), array([559, 241]))
In [9]:
train_y_proportion = np.unique(y_train, return_counts=True)[1] \
    / np.sum(np.unique(y_train, return_counts=True)[1])
logging.info(f"balance of y in training: {train_y_proportion}")
2023-11-24 20:59:39 - INFO     | balance of y in training: [0.69875 0.30125]
In [10]:
test_y_proportion = np.unique(y_test, return_counts=True)[1] \
    / np.sum(np.unique(y_test, return_counts=True)[1])
logging.info(f"balance of y in test: {test_y_proportion}")
2023-11-24 20:59:39 - INFO     | balance of y in test: [0.705 0.295]

Cross Validation Results¶

Best Scores/Params¶

In [11]:
results = experiment.last_run.download_artifact(
    artifact_name='experiment.yaml',
    read_from=MLExperimentResults.from_yaml_file,
)
logging.info(f"Best Score: {results.best_score}")
logging.info(f"Best Params: {results.best_params}")
2023-11-24 20:59:39 - INFO     | Best Score: 0.753377535324465
2023-11-24 20:59:39 - INFO     | Best Params: {'model': 'RandomForestClassifier()', 'imputer': 'SimpleImputer()', 'scaler': 'None', 'pca': 'None', 'savings_status_encoder': 'OneHotEncoder()'}
In [12]:
# Best model from each model-type.
data = results.to_formatted_dataframe(return_style=False, include_rank=True)
data["model_rank"] = data.groupby("model")["roc_auc Mean"].rank(method="first", ascending=False)
data.query('model_rank == 1')
Out[12]:
rank roc_auc Mean roc_auc 95CI.LO roc_auc 95CI.HI model C max_features max_depth n_estimators min_samples_split ... colsample_bytree colsample_bylevel reg_alpha reg_lambda num_leaves imputer scaler pca savings_status_encoder model_rank
10 1 0.753 0.649 0.857 RandomForestClassifier() NaN NaN NaN NaN NaN ... NaN NaN NaN NaN NaN SimpleImputer() None None OneHotEncoder() 1.0
5 3 0.750 0.656 0.845 ExtraTreesClassifier() NaN NaN NaN NaN NaN ... NaN NaN NaN NaN NaN SimpleImputer() None None OneHotEncoder() 1.0
0 5 0.744 0.641 0.847 LogisticRegression() NaN NaN NaN NaN NaN ... NaN NaN NaN NaN NaN SimpleImputer() StandardScaler() None OneHotEncoder() 1.0
19 6 0.744 0.640 0.848 XGBClassifier() NaN NaN 1.0 1315.0 NaN ... 0.530125 0.985952 0.001700 2.777691 NaN SimpleImputer(strategy='median') None None SavingsStatusEncoder() 1.0
23 12 0.732 0.613 0.850 LGBMClassifier() NaN NaN NaN NaN NaN ... 0.745611 NaN 0.877292 47.641776 497.0 SimpleImputer(strategy='median') None None OneHotEncoder() 1.0

5 rows × 26 columns

In [13]:
results.to_formatted_dataframe(return_style=True,
                               include_rank=True,
                               num_rows=500)
Out[13]:
rank roc_auc Mean roc_auc 95CI.LO roc_auc 95CI.HI model C max_features max_depth n_estimators min_samples_split min_samples_leaf max_samples criterion learning_rate min_child_weight subsample colsample_bytree colsample_bylevel reg_alpha reg_lambda num_leaves imputer scaler pca savings_status_encoder
1 0.753 0.649 0.857 RandomForestClassifier() <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> SimpleImputer() None None OneHotEncoder()
2 0.751 0.624 0.878 RandomForestClassifier() <NA> 0.583 35.000 1,474.000 22.000 5.000 0.765 entropy <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> SimpleImputer(strategy='median') None PCA('mle') OneHotEncoder()
3 0.750 0.656 0.845 ExtraTreesClassifier() <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> SimpleImputer() None None OneHotEncoder()
4 0.747 0.631 0.863 RandomForestClassifier() <NA> 0.239 41.000 1,886.000 3.000 15.000 0.864 gini <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> SimpleImputer() None PCA('mle') SavingsStatusEncoder()
5 0.744 0.641 0.847 LogisticRegression() <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> SimpleImputer() StandardScaler() None OneHotEncoder()
6 0.744 0.640 0.848 XGBClassifier() <NA> <NA> 1.000 1,315.000 <NA> <NA> <NA> <NA> 0.022 17.000 0.716 0.530 0.986 0.002 2.778 <NA> SimpleImputer(strategy='median') None None SavingsStatusEncoder()
7 0.743 0.703 0.783 LogisticRegression() 0.000 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> SimpleImputer() MinMaxScaler() None SavingsStatusEncoder()
8 0.739 0.606 0.872 RandomForestClassifier() <NA> 0.911 74.000 1,265.000 39.000 17.000 0.751 entropy <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> SimpleImputer() None PCA('mle') OneHotEncoder()
9 0.739 0.626 0.851 RandomForestClassifier() <NA> 0.445 87.000 1,244.000 33.000 27.000 0.795 gini <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> SimpleImputer() None PCA('mle') OneHotEncoder()
10 0.734 0.672 0.796 ExtraTreesClassifier() <NA> 0.135 15.000 1,987.000 10.000 39.000 0.708 gini <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> SimpleImputer(strategy='most_frequent') None None SavingsStatusEncoder()
11 0.732 0.606 0.859 XGBClassifier() <NA> <NA> 20.000 1,733.000 <NA> <NA> <NA> <NA> 0.010 2.000 0.673 0.772 0.881 0.281 2.005 <NA> SimpleImputer() None PCA('mle') OneHotEncoder()
12 0.732 0.613 0.850 LGBMClassifier() <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> 0.732 0.746 <NA> 0.877 47.642 497.000 SimpleImputer(strategy='median') None None OneHotEncoder()
13 0.731 0.646 0.816 LGBMClassifier() <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> 0.630 0.248 <NA> 5.830 44.985 347.000 SimpleImputer(strategy='most_frequent') None None OneHotEncoder()
14 0.727 0.633 0.821 ExtraTreesClassifier() <NA> 0.502 29.000 1,249.000 25.000 35.000 0.855 gini <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> SimpleImputer(strategy='median') None None OneHotEncoder()
15 0.725 0.590 0.860 LogisticRegression() 0.000 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> SimpleImputer(strategy='most_frequent') StandardScaler() PCA('mle') SavingsStatusEncoder()
16 0.723 0.585 0.862 LogisticRegression() 0.000 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> SimpleImputer(strategy='median') StandardScaler() None SavingsStatusEncoder()
17 0.723 0.588 0.859 LogisticRegression() 0.000 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> SimpleImputer(strategy='median') StandardScaler() PCA('mle') SavingsStatusEncoder()
18 0.723 0.633 0.813 ExtraTreesClassifier() <NA> 0.537 2.000 1,275.000 14.000 47.000 0.805 entropy <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> SimpleImputer(strategy='median') None None SavingsStatusEncoder()
19 0.722 0.637 0.808 ExtraTreesClassifier() <NA> 0.768 54.000 909.000 16.000 30.000 0.762 entropy <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> SimpleImputer(strategy='median') None PCA('mle') SavingsStatusEncoder()
20 0.721 0.622 0.819 XGBClassifier() <NA> <NA> 3.000 1,482.000 <NA> <NA> <NA> <NA> 0.067 18.000 0.889 0.636 0.615 0.000 2.093 <NA> SimpleImputer(strategy='median') None None OneHotEncoder()
21 0.715 0.667 0.764 LGBMClassifier() <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> 0.429 0.877 <NA> 13.997 35.364 388.000 SimpleImputer(strategy='most_frequent') None None OneHotEncoder()
22 0.713 0.604 0.823 LGBMClassifier() <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> SimpleImputer() None None OneHotEncoder()
23 0.701 0.587 0.814 XGBClassifier() <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> SimpleImputer() None None OneHotEncoder()
24 0.691 0.638 0.745 LGBMClassifier() <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> 0.965 0.241 <NA> 19.400 3.771 281.000 SimpleImputer(strategy='median') None PCA('mle') OneHotEncoder()
25 0.681 0.582 0.780 XGBClassifier() <NA> <NA> 4.000 1,961.000 <NA> <NA> <NA> <NA> 0.271 3.000 0.671 0.797 0.696 0.000 2.150 <NA> SimpleImputer(strategy='median') None PCA('mle') SavingsStatusEncoder()
In [14]:
results.to_formatted_dataframe(query='model == "RandomForestClassifier()"', include_rank=True)
Out[14]:
rank roc_auc Mean roc_auc 95CI.LO roc_auc 95CI.HI max_features max_depth n_estimators min_samples_split min_samples_leaf max_samples criterion imputer pca savings_status_encoder
1 0.753 0.649 0.857 <NA> <NA> <NA> <NA> <NA> <NA> <NA> SimpleImputer() None OneHotEncoder()
2 0.751 0.624 0.878 0.583 35.000 1,474.000 22.000 5.000 0.765 entropy SimpleImputer(strategy='median') PCA('mle') OneHotEncoder()
3 0.747 0.631 0.863 0.239 41.000 1,886.000 3.000 15.000 0.864 gini SimpleImputer() PCA('mle') SavingsStatusEncoder()
4 0.739 0.606 0.872 0.911 74.000 1,265.000 39.000 17.000 0.751 entropy SimpleImputer() PCA('mle') OneHotEncoder()
5 0.739 0.626 0.851 0.445 87.000 1,244.000 33.000 27.000 0.795 gini SimpleImputer() PCA('mle') OneHotEncoder()
In [15]:
results.to_formatted_dataframe(query='model == "LogisticRegression()"', include_rank=True)
Out[15]:
rank roc_auc Mean roc_auc 95CI.LO roc_auc 95CI.HI C imputer scaler pca savings_status_encoder
1 0.744 0.641 0.847 <NA> SimpleImputer() StandardScaler() None OneHotEncoder()
2 0.743 0.703 0.783 0.000 SimpleImputer() MinMaxScaler() None SavingsStatusEncoder()
3 0.725 0.590 0.860 0.000 SimpleImputer(strategy='most_frequent') StandardScaler() PCA('mle') SavingsStatusEncoder()
4 0.723 0.585 0.862 0.000 SimpleImputer(strategy='median') StandardScaler() None SavingsStatusEncoder()
5 0.723 0.588 0.859 0.000 SimpleImputer(strategy='median') StandardScaler() PCA('mle') SavingsStatusEncoder()

BayesSearchCV Performance Over Time¶

In [16]:
results.plot_performance_across_trials(facet_by='model').show()
In [17]:
results.plot_performance_across_trials(query='model == "RandomForestClassifier()"').show()

Variable Performance Over Time¶

In [18]:
results.plot_parameter_values_across_trials(query='model == "RandomForestClassifier()"').show()

Scatter Matrix¶

In [19]:
# results.plot_scatter_matrix(query='model == "RandomForestClassifier()"',
#                             height=1000, width=1000).show()

Variable Performance - Numeric¶

In [20]:
results.plot_performance_numeric_params(query='model == "RandomForestClassifier()"',
                                        height=800)
In [21]:
results.plot_parallel_coordinates(query='model == "RandomForestClassifier()"').show()

Variable Performance - Non-Numeric¶

In [22]:
results.plot_performance_non_numeric_params(query='model == "RandomForestClassifier()"').show()

In [23]:
results.plot_score_vs_parameter(
    query='model == "RandomForestClassifier()"',
    parameter='max_features',
    size='max_depth',
    color='savings_status_encoder',
)

In [24]:
# results.plot_parameter_vs_parameter(
#     query='model == "XGBClassifier()"',
#     parameter_x='colsample_bytree',
#     parameter_y='learning_rate',
#     size='max_depth'
# )
In [25]:
# results.plot_parameter_vs_parameter(
#     query='model == "XGBClassifier()"',
#     parameter_x='colsample_bytree',
#     parameter_y='learning_rate',
#     size='imputer'
# )

Last Run - Test Set Performance¶

In [26]:
last_model = experiment.last_run.download_artifact(
    artifact_name='model/model.pkl',
    read_from=read_pickle,
)
print(type(last_model.model))
<class 'sklearn.pipeline.Pipeline'>
In [27]:
last_model
Out[27]:
SklearnModelWrapper(model=Pipeline(steps=[('prep',
                                           ColumnTransformer(transformers=[('numeric',
                                                                            Pipeline(steps=[('imputer',
                                                                                             TransformerChooser(transformer=SimpleImputer())),
                                                                                            ('scaler',
                                                                                             TransformerChooser()),
                                                                                            ('pca',
                                                                                             TransformerChooser())]),
                                                                            ['duration',
                                                                             'credit_amount',
                                                                             'installment_commitment',
                                                                             'residence_since',
                                                                             'age',
                                                                             'existing_credits',
                                                                             'num_dependents']),
                                                                           ('n...
                                                                             'employment',
                                                                             'personal_status',
                                                                             'other_parties',
                                                                             'property_magnitude',
                                                                             'other_payment_plans',
                                                                             'housing',
                                                                             'job',
                                                                             'own_telephone',
                                                                             'foreign_worker']),
                                                                           ('savings_status',
                                                                            Pipeline(steps=[('savings_encoder',
                                                                                             TransformerChooser(transformer=OneHotEncoder(handle_unknown='ignore')))]),
                                                                            ['savings_status'])])),
                                          ('model',
                                           RandomForestClassifier(n_estimators=500,
                                                                  random_state=42))]))
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
SklearnModelWrapper(model=Pipeline(steps=[('prep',
                                           ColumnTransformer(transformers=[('numeric',
                                                                            Pipeline(steps=[('imputer',
                                                                                             TransformerChooser(transformer=SimpleImputer())),
                                                                                            ('scaler',
                                                                                             TransformerChooser()),
                                                                                            ('pca',
                                                                                             TransformerChooser())]),
                                                                            ['duration',
                                                                             'credit_amount',
                                                                             'installment_commitment',
                                                                             'residence_since',
                                                                             'age',
                                                                             'existing_credits',
                                                                             'num_dependents']),
                                                                           ('n...
                                                                             'employment',
                                                                             'personal_status',
                                                                             'other_parties',
                                                                             'property_magnitude',
                                                                             'other_payment_plans',
                                                                             'housing',
                                                                             'job',
                                                                             'own_telephone',
                                                                             'foreign_worker']),
                                                                           ('savings_status',
                                                                            Pipeline(steps=[('savings_encoder',
                                                                                             TransformerChooser(transformer=OneHotEncoder(handle_unknown='ignore')))]),
                                                                            ['savings_status'])])),
                                          ('model',
                                           RandomForestClassifier(n_estimators=500,
                                                                  random_state=42))]))
Pipeline(steps=[('prep',
                 ColumnTransformer(transformers=[('numeric',
                                                  Pipeline(steps=[('imputer',
                                                                   TransformerChooser(transformer=SimpleImputer())),
                                                                  ('scaler',
                                                                   TransformerChooser()),
                                                                  ('pca',
                                                                   TransformerChooser())]),
                                                  ['duration', 'credit_amount',
                                                   'installment_commitment',
                                                   'residence_since', 'age',
                                                   'existing_credits',
                                                   'num_dependents']),
                                                 ('non_numeric',
                                                  Pipeline(steps...
                                                   'employment',
                                                   'personal_status',
                                                   'other_parties',
                                                   'property_magnitude',
                                                   'other_payment_plans',
                                                   'housing', 'job',
                                                   'own_telephone',
                                                   'foreign_worker']),
                                                 ('savings_status',
                                                  Pipeline(steps=[('savings_encoder',
                                                                   TransformerChooser(transformer=OneHotEncoder(handle_unknown='ignore')))]),
                                                  ['savings_status'])])),
                ('model',
                 RandomForestClassifier(n_estimators=500, random_state=42))])
ColumnTransformer(transformers=[('numeric',
                                 Pipeline(steps=[('imputer',
                                                  TransformerChooser(transformer=SimpleImputer())),
                                                 ('scaler',
                                                  TransformerChooser()),
                                                 ('pca',
                                                  TransformerChooser())]),
                                 ['duration', 'credit_amount',
                                  'installment_commitment', 'residence_since',
                                  'age', 'existing_credits',
                                  'num_dependents']),
                                ('non_numeric',
                                 Pipeline(steps=[('encoder',
                                                  OneHotEncod...n='ignore'))]),
                                 ['checking_status', 'credit_history',
                                  'purpose', 'employment', 'personal_status',
                                  'other_parties', 'property_magnitude',
                                  'other_payment_plans', 'housing', 'job',
                                  'own_telephone', 'foreign_worker']),
                                ('savings_status',
                                 Pipeline(steps=[('savings_encoder',
                                                  TransformerChooser(transformer=OneHotEncoder(handle_unknown='ignore')))]),
                                 ['savings_status'])])
['duration', 'credit_amount', 'installment_commitment', 'residence_since', 'age', 'existing_credits', 'num_dependents']
TransformerChooser(transformer=SimpleImputer())
SimpleImputer()
SimpleImputer()
TransformerChooser()
TransformerChooser()
['checking_status', 'credit_history', 'purpose', 'employment', 'personal_status', 'other_parties', 'property_magnitude', 'other_payment_plans', 'housing', 'job', 'own_telephone', 'foreign_worker']
OneHotEncoder(handle_unknown='ignore')
['savings_status']
TransformerChooser(transformer=OneHotEncoder(handle_unknown='ignore'))
OneHotEncoder(handle_unknown='ignore')
OneHotEncoder(handle_unknown='ignore')
RandomForestClassifier(n_estimators=500, random_state=42)
In [28]:
test_predictions = last_model.predict(X_test)
test_predictions[0:10]
Out[28]:
array([0.408, 0.522, 0.678, 0.404, 0.088, 0.454, 0.092, 0.492, 0.176,
       0.232])
In [29]:
evaluator = hlp.sklearn_eval.TwoClassEvaluator(
    actual_values=y_test,
    predicted_scores=test_predictions,
    score_threshold=0.37,
)
In [30]:
evaluator.plot_actual_vs_predict_histogram()
In [31]:
evaluator.plot_confusion_matrix()
No description has been provided for this image
In [32]:
evaluator.all_metrics_df(return_style=True,
                         dummy_classifier_strategy=['prior', 'constant'],
                         round_by=3)
Out[32]:
  Score Dummy (prior) Dummy (constant) Explanation
AUC 0.815 0.500 0.500 Area under the ROC curve (true pos. rate vs false pos. rate); ranges from 0.5 (purely random classifier) to 1.0 (perfect classifier)
True Positive Rate 0.729 0.000 1.000 72.9% of positive instances were correctly identified.; i.e. 43 "Positive Class" labels were correctly identified out of 59 instances; a.k.a Sensitivity/Recall
True Negative Rate 0.801 1.000 0.000 80.1% of negative instances were correctly identified.; i.e. 113 "Negative Class" labels were correctly identified out of 141 instances
False Positive Rate 0.199 0.000 1.000 19.9% of negative instances were incorrectly identified as positive; i.e. 28 "Negative Class" labels were incorrectly identified as "Positive Class", out of 141 instances
False Negative Rate 0.271 1.000 0.000 27.1% of positive instances were incorrectly identified as negative; i.e. 16 "Positive Class" labels were incorrectly identified as "Negative Class", out of 59 instances
Positive Predictive Value 0.606 0.000 0.295 When the model claims an instance is positive, it is correct 60.6% of the time; i.e. out of the 71 times the model predicted "Positive Class", it was correct 43 times; a.k.a precision
Negative Predictive Value 0.876 0.705 0.000 When the model claims an instance is negative, it is correct 87.6% of the time; i.e. out of the 129 times the model predicted "Negative Class", it was correct 113 times
F1 Score 0.662 0.000 0.456 The F1 score can be interpreted as a weighted average of the precision and recall, where an F1 score reaches its best value at 1 and worst score at 0.
Precision/Recall AUC 0.660 0.295 0.295 Precision/Recall AUC is calculated with `average_precision` which summarizes a precision-recall curve as the weighted mean of precisions achieved at each threshold. See sci-kit learn documentation for caveats.
Accuracy 0.780 0.705 0.295 78.0% of instances were correctly identified
Error Rate 0.220 0.295 0.705 22.0% of instances were incorrectly identified
% Positive 0.295 0.295 0.295 29.5% of the data are positive; i.e. out of 200 total observations; 59 are labeled as "Positive Class"
Total Observations 200 200 200 There are 200 total observations; i.e. sample size
In [33]:
evaluator.plot_roc_auc_curve().show()
In [34]:
evaluator.plot_precision_recall_auc_curve().show()
In [35]:
evaluator.plot_threshold_curves(score_threshold_range=(0.1, 0.7)).show()
In [36]:
evaluator.plot_precision_recall_tradeoff(score_threshold_range=(0.1, 0.6)).show()
In [37]:
evaluator.calculate_lift_gain(return_style=True)
/usr/local/lib/python3.11/site-packages/helpsk/sklearn_eval.py:2480: FutureWarning:

The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.

Out[37]:
  Gain Lift
Percentile    
5 0.14 2.71
10 0.22 2.20
15 0.36 2.37
20 0.51 2.54
25 0.54 2.17
30 0.68 2.26
35 0.73 2.08
40 0.76 1.91
45 0.76 1.69
50 0.81 1.63
55 0.85 1.54
60 0.85 1.41
65 0.88 1.36
70 0.90 1.28
75 0.95 1.27
80 0.97 1.21
85 0.98 1.16
90 0.98 1.09
95 1.00 1.05
100 1.00 1.00

Production Model - Test Set Performance¶

In [38]:
production_model = production_run.download_artifact(
    artifact_name='model/model.pkl',
    read_from=read_pickle,
)
print(type(production_model.model))
<class 'sklearn.pipeline.Pipeline'>
In [39]:
production_model
Out[39]:
SklearnModelWrapper(model=Pipeline(steps=[('prep',
                                           ColumnTransformer(transformers=[('numeric',
                                                                            Pipeline(steps=[('imputer',
                                                                                             TransformerChooser(transformer=SimpleImputer())),
                                                                                            ('scaler',
                                                                                             TransformerChooser()),
                                                                                            ('pca',
                                                                                             TransformerChooser())]),
                                                                            ['duration',
                                                                             'credit_amount',
                                                                             'installment_commitment',
                                                                             'residence_since',
                                                                             'age',
                                                                             'existing_credits',
                                                                             'num_dependents']),
                                                                           ('n...
                                                                             'employment',
                                                                             'personal_status',
                                                                             'other_parties',
                                                                             'property_magnitude',
                                                                             'other_payment_plans',
                                                                             'housing',
                                                                             'job',
                                                                             'own_telephone',
                                                                             'foreign_worker']),
                                                                           ('savings_status',
                                                                            Pipeline(steps=[('savings_encoder',
                                                                                             TransformerChooser(transformer=OneHotEncoder(handle_unknown='ignore')))]),
                                                                            ['savings_status'])])),
                                          ('model',
                                           RandomForestClassifier(n_estimators=500,
                                                                  random_state=42))]))
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
SklearnModelWrapper(model=Pipeline(steps=[('prep',
                                           ColumnTransformer(transformers=[('numeric',
                                                                            Pipeline(steps=[('imputer',
                                                                                             TransformerChooser(transformer=SimpleImputer())),
                                                                                            ('scaler',
                                                                                             TransformerChooser()),
                                                                                            ('pca',
                                                                                             TransformerChooser())]),
                                                                            ['duration',
                                                                             'credit_amount',
                                                                             'installment_commitment',
                                                                             'residence_since',
                                                                             'age',
                                                                             'existing_credits',
                                                                             'num_dependents']),
                                                                           ('n...
                                                                             'employment',
                                                                             'personal_status',
                                                                             'other_parties',
                                                                             'property_magnitude',
                                                                             'other_payment_plans',
                                                                             'housing',
                                                                             'job',
                                                                             'own_telephone',
                                                                             'foreign_worker']),
                                                                           ('savings_status',
                                                                            Pipeline(steps=[('savings_encoder',
                                                                                             TransformerChooser(transformer=OneHotEncoder(handle_unknown='ignore')))]),
                                                                            ['savings_status'])])),
                                          ('model',
                                           RandomForestClassifier(n_estimators=500,
                                                                  random_state=42))]))
Pipeline(steps=[('prep',
                 ColumnTransformer(transformers=[('numeric',
                                                  Pipeline(steps=[('imputer',
                                                                   TransformerChooser(transformer=SimpleImputer())),
                                                                  ('scaler',
                                                                   TransformerChooser()),
                                                                  ('pca',
                                                                   TransformerChooser())]),
                                                  ['duration', 'credit_amount',
                                                   'installment_commitment',
                                                   'residence_since', 'age',
                                                   'existing_credits',
                                                   'num_dependents']),
                                                 ('non_numeric',
                                                  Pipeline(steps...
                                                   'employment',
                                                   'personal_status',
                                                   'other_parties',
                                                   'property_magnitude',
                                                   'other_payment_plans',
                                                   'housing', 'job',
                                                   'own_telephone',
                                                   'foreign_worker']),
                                                 ('savings_status',
                                                  Pipeline(steps=[('savings_encoder',
                                                                   TransformerChooser(transformer=OneHotEncoder(handle_unknown='ignore')))]),
                                                  ['savings_status'])])),
                ('model',
                 RandomForestClassifier(n_estimators=500, random_state=42))])
ColumnTransformer(transformers=[('numeric',
                                 Pipeline(steps=[('imputer',
                                                  TransformerChooser(transformer=SimpleImputer())),
                                                 ('scaler',
                                                  TransformerChooser()),
                                                 ('pca',
                                                  TransformerChooser())]),
                                 ['duration', 'credit_amount',
                                  'installment_commitment', 'residence_since',
                                  'age', 'existing_credits',
                                  'num_dependents']),
                                ('non_numeric',
                                 Pipeline(steps=[('encoder',
                                                  OneHotEncod...n='ignore'))]),
                                 ['checking_status', 'credit_history',
                                  'purpose', 'employment', 'personal_status',
                                  'other_parties', 'property_magnitude',
                                  'other_payment_plans', 'housing', 'job',
                                  'own_telephone', 'foreign_worker']),
                                ('savings_status',
                                 Pipeline(steps=[('savings_encoder',
                                                  TransformerChooser(transformer=OneHotEncoder(handle_unknown='ignore')))]),
                                 ['savings_status'])])
['duration', 'credit_amount', 'installment_commitment', 'residence_since', 'age', 'existing_credits', 'num_dependents']
TransformerChooser(transformer=SimpleImputer())
SimpleImputer()
SimpleImputer()
TransformerChooser()
TransformerChooser()
['checking_status', 'credit_history', 'purpose', 'employment', 'personal_status', 'other_parties', 'property_magnitude', 'other_payment_plans', 'housing', 'job', 'own_telephone', 'foreign_worker']
OneHotEncoder(handle_unknown='ignore')
['savings_status']
TransformerChooser(transformer=OneHotEncoder(handle_unknown='ignore'))
OneHotEncoder(handle_unknown='ignore')
OneHotEncoder(handle_unknown='ignore')
RandomForestClassifier(n_estimators=500, random_state=42)
In [40]:
test_predictions = production_model.predict(X_test)
test_predictions[0:10]
Out[40]:
array([0.408, 0.522, 0.678, 0.404, 0.088, 0.454, 0.092, 0.492, 0.176,
       0.232])
In [41]:
evaluator = hlp.sklearn_eval.TwoClassEvaluator(
    actual_values=y_test,
    predicted_scores=test_predictions,
    score_threshold=0.37,
)
In [42]:
evaluator.plot_actual_vs_predict_histogram()
In [43]:
evaluator.plot_confusion_matrix()
No description has been provided for this image
In [44]:
evaluator.all_metrics_df(return_style=True,
                         dummy_classifier_strategy=['prior', 'constant'],
                         round_by=3)
Out[44]:
  Score Dummy (prior) Dummy (constant) Explanation
AUC 0.815 0.500 0.500 Area under the ROC curve (true pos. rate vs false pos. rate); ranges from 0.5 (purely random classifier) to 1.0 (perfect classifier)
True Positive Rate 0.729 0.000 1.000 72.9% of positive instances were correctly identified.; i.e. 43 "Positive Class" labels were correctly identified out of 59 instances; a.k.a Sensitivity/Recall
True Negative Rate 0.801 1.000 0.000 80.1% of negative instances were correctly identified.; i.e. 113 "Negative Class" labels were correctly identified out of 141 instances
False Positive Rate 0.199 0.000 1.000 19.9% of negative instances were incorrectly identified as positive; i.e. 28 "Negative Class" labels were incorrectly identified as "Positive Class", out of 141 instances
False Negative Rate 0.271 1.000 0.000 27.1% of positive instances were incorrectly identified as negative; i.e. 16 "Positive Class" labels were incorrectly identified as "Negative Class", out of 59 instances
Positive Predictive Value 0.606 0.000 0.295 When the model claims an instance is positive, it is correct 60.6% of the time; i.e. out of the 71 times the model predicted "Positive Class", it was correct 43 times; a.k.a precision
Negative Predictive Value 0.876 0.705 0.000 When the model claims an instance is negative, it is correct 87.6% of the time; i.e. out of the 129 times the model predicted "Negative Class", it was correct 113 times
F1 Score 0.662 0.000 0.456 The F1 score can be interpreted as a weighted average of the precision and recall, where an F1 score reaches its best value at 1 and worst score at 0.
Precision/Recall AUC 0.660 0.295 0.295 Precision/Recall AUC is calculated with `average_precision` which summarizes a precision-recall curve as the weighted mean of precisions achieved at each threshold. See sci-kit learn documentation for caveats.
Accuracy 0.780 0.705 0.295 78.0% of instances were correctly identified
Error Rate 0.220 0.295 0.705 22.0% of instances were incorrectly identified
% Positive 0.295 0.295 0.295 29.5% of the data are positive; i.e. out of 200 total observations; 59 are labeled as "Positive Class"
Total Observations 200 200 200 There are 200 total observations; i.e. sample size
In [45]:
evaluator.plot_roc_auc_curve().show()
In [46]:
evaluator.plot_precision_recall_auc_curve().show()
In [47]:
evaluator.plot_threshold_curves(score_threshold_range=(0.1, 0.7)).show()
In [48]:
evaluator.plot_precision_recall_tradeoff(score_threshold_range=(0.1, 0.6)).show()
In [49]:
evaluator.calculate_lift_gain(return_style=True)
/usr/local/lib/python3.11/site-packages/helpsk/sklearn_eval.py:2480: FutureWarning:

The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.

Out[49]:
  Gain Lift
Percentile    
5 0.14 2.71
10 0.22 2.20
15 0.36 2.37
20 0.51 2.54
25 0.54 2.17
30 0.68 2.26
35 0.73 2.08
40 0.76 1.91
45 0.76 1.69
50 0.81 1.63
55 0.85 1.54
60 0.85 1.41
65 0.88 1.36
70 0.90 1.28
75 0.95 1.27
80 0.97 1.21
85 0.98 1.16
90 0.98 1.09
95 1.00 1.05
100 1.00 1.00

Feature Importance¶

In [50]:
try:
    importances = production_model.model['model'].feature_importances_
    feature_names = [
        x.replace('non_numeric__', '').replace('numeric__', '')
        for x in production_model.model[:-1].get_feature_names_out()
    ]
    feature_importances = sorted(
        zip(feature_names, importances, strict=True),
        key=lambda x: x[1],
        reverse=False,
    )
    fig = px.bar(
        pd.DataFrame(feature_importances, columns=['feature', 'importance']).tail(20),
        y='feature',
        x='importance',
        orientation='h',
        height=700,
        width=800,
        title='Feature Importances of Production Model',
    )
    fig.show()
except:  # noqa
    print("Error calculating feature importances.")